1. Field of the Invention
The present invention also generally relates to computerized techniques for automated analysis of digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; 5,732,697; 5,740,268; 5,790,690; 5,832,103; 5,873,824; 5,881,124; 5,931,780; 5,974,165; 5,982,915; 5,984,870; 5,987,345; 6,011,862; 6,058,322; 6,067,373; 6,075,878; 6,078,680; 6,088,473; 6,112,112; 6,138,045; 6,141,437; 6,185,320; 6,205,348; 6,240,201; 6,282,305; 6,282,307; 6,317,617 as well as U.S. Pat. No. 6,466,689; 08/398,307 (PCT Publication WO 96/27846); U.S. Pat. No. 5,719,898; Ser. No. 08/900,189; U.S. Pat. Nos. 6,363,163; 6,442,287; 6,335,980; 6,594,378; 6,470,092; Ser. Nos. 09/759,333; 09/760,854; 09/773,636; 09/816,217; 09/830,562; 09/818,831; U.S. Pat. No. 6,483,936; Ser. Nos. 09/860,574; 60/160,790; 60/176,304; and 60/329,322; co-pending application Ser. Nos. 09/990,310, 09/990,377, 10/126,523 PROV, and 10/301,836 PROV; and PCT patent applications PCT/US98/15165; PCT/US98/24933; PCT/US99/03287; PCT/US00/41299; PCT/US01/00680; PCT/US01/01478 and PCT/US01/01479, all of which are incorporated herein by reference.
The present invention includes use of various technologies referenced and described in the above-noted U.S. Patents and Applications, as well as described in the references identified in the following LIST OF REFERENCES by the author(s) and year of publication and cross-referenced throughout the specification by reference to the respective number, in parentheses, of the reference:
1. R. T. Greenlee, M. B. Hill-Harmon, T. Murray, and M. Thun, xe2x80x9cCancer statistics, 200,xe2x80x9d Ca-Cancer J Clin 51, 15-36 (2001).
2. C I. Henschke, D. I. McCauley, D. F. Yankelevitz, D. P. Naidich, G. Guinness, O. S. Miettinen, D. M. Libby, M. W. Pasmantier, J. Koizumi, N. K. Altorki, and J. P. Smith, xe2x80x9cEarly lung cancer action project: overall design and findings from baseline screening,xe2x80x9d The Lancet, 354, Jul. 10, 99-105, (1999).
3. D. P. Naidich, C. H. Marshall, C. Gribbin, R. Arams and D. I. McCauley, xe2x80x9cLow-dose CT of the lungs: preliminary observations,xe2x80x9d Radiology 175, 729-731, (1990).
4. D. F. Yankelevitz, R. Gupta, B. Zhao and C. I. Henschke, xe2x80x9cSmall pulmonary nodules: Evaluation with repeat CT-preliminary experience,xe2x80x9d Radiology 217, 251-256 (2000).
5. C. I. Henschke, xe2x80x9cEarly lung cancer action project: overall design and findings from baseline screening,xe2x80x9d Cancer 89, 2474-2482 (2000).
6. C. I. Henschke, D. P. Naidich, D. F. Yankelevitz, G. McGuinness, D. I. McCauley, J. P. Smith, D. Libby, M. Pasmantier, M. Vazquez, J. Koizumi, D. Flieder, N. K. Altorki and O. S. Miettinen, xe2x80x9cEarly lung cancer action project: initial findings on repeat screenings,xe2x80x9d Cancer 92, 153-159 (2001).
7. C. I. Henschke, D. I. McCauley, D. F. Yankelevitz, D. P. Naidich, G. McGuinness, O. S. Miettinen, D. Libby, M. Pasmantier, J. Koizumi, N. K. Altorki and J. P. Smith, xe2x80x9cEarly lung cancer action project: a summary of the findings on baseline screening,xe2x80x9d Oncologist 6, 147-152 (2001).
8. D. Marshall, K. N. Simpson, C. C. Earle and C. W. Chu, xe2x80x9cEconomic decision analysis model of screening for lung cancer,xe2x80x9d Eur J Cancer 37, 1759-1767 (2001).
9. S. G. Armato, M. L. Giger, C. J. Moran, J. T. Blackburn, K. Doi and H. MacMahon, xe2x80x9cA computerized detection of pulmonary nodules on CT scans,xe2x80x9d Radiographics 19, 1303-1311 (1999).
10. T. K. Narayan and G. T. Herman, xe2x80x9cThe use of contrast for automated pulmonary nodule detection in low-dose computed tomography,xe2x80x9d Med Phys 26, 427-437 (1999).
11. S. G. Armato, M. L. Giger and H. MacMahon, xe2x80x9cAutomated detection of lung nodules in CT scans: preliminary results,xe2x80x9d Med Phys 28, 1552-61 (2001).
12. S. Yamamoto, I. Tanaka, M. Senda, Y. Tateno, T. linuma and T. Matsumoto, xe2x80x9cImage processing for computer-aided diagnosis of lung cancer by CT (LSCT),xe2x80x9d Systems and Computers in Japan 25, 67-80 (1994).
13. S. Yamamoto, M. Matsumoto, Y. Tateno et al., xe2x80x9cQuoit filter: A new filter based on mathematical morphology to extract the isolated shadow, and its application to automatic detection of lung cancer in X-ray CT,xe2x80x9d Proc ICPR II 3-7 (1996).
14. T. Tozaki, Y. Kawata, N. Niki, et al., xe2x80x9cPulmonary organs analysis for differential diagnosis based on thoracic thin-section CT images,xe2x80x9d IEEE Trans Nuclear Science 45, 3075-3082 (1998).
15. Y. Kawata, N. Niki, H. Ohmatsu et al., xe2x80x9cQuantitative surface characterization of pulmonary nodules based on thin-section CT images,xe2x80x9d IEEE Trans Nuclear Science 45, 2132-2138 (1998).
16. A. Kano, K. Doi, H. MacMahon, D. D. Hassell, and M. L. Giger, xe2x80x9cDigital image subtraction of temporally sequential chest images for detection of interval change,xe2x80x9d Med. Phys. 21, 453-461, (1994).
17. T. Ishida, S. Katsuragawa, K. Nakamura, H. MacMahon and K. Doi, xe2x80x9cIterative image-warping technique for temporal subtraction of sequential chest radiographs to detect interval change,xe2x80x9d Medical Physics, 26, 1320-1329 (1999).
18. T. Ishida, K. Ashizawa, R. Engelmann, S. Katsuragawa, H. MacMahon and K. Doi, xe2x80x9cApplication of temporal subtraction for detection of interval changes in chest radiographs: Improvement of subtraction image using automated initial image matching,xe2x80x9d Journal of Digital Imaging, 12, 77-86, (1999).
19. S. Sone, S. Takashima, F. Li, et al., xe2x80x9cMass screening for lung cancer with mobile spiral computed tomography scanner,xe2x80x9d Lancet, 351, 1242-1245, (1998).
20. Sone S, Li F, Yang Z-G, et al., xe2x80x9cResults of three-year mass screening programmed for lung cancer using mobile low-dose spiral computed tomography scanner,xe2x80x9d Br. J. Cancer, 84:25-32 (2001).
21. S. Katsuragawa, K. Doi, H. MacMahon, L. Monnier-Cholley, J. Morishita, and T. Ishida, xe2x80x9cQuantitative analysis of geometric-pattern features of interstitial infiltrates indigital chest radiographs: preliminary results,xe2x80x9d Journal of Digital Imaging, 9, 137-144 (1996).
22. T. Ishida, S. Katsuragawa, T. Kabayashi, H. MacMahon and K. Doi, xe2x80x9cComputerized analysis of interstitial disease in chest radiographs: Improvement of geometric pattern feature analysis,xe2x80x9d Medical Physics 24, 915-924 (1997).
23. H. Yoshimura, M. L. Giger, K. Doi, H. MacMahon and S. M. Montner, xe2x80x9cComputerized scheme for the detection of pulmonary nodules: Nonlinear filtering technique,xe2x80x9d Invest Radiol 27, 124-129 (1992).
The entire contents of each related patent and application listed above and each reference listed in the LIST OF REFERENCES, are incorporated herein by reference.
2. Discussion of the Background
Recently, medical professionals have been able to diagnose lung cancer with the aid of computed tomography (CT) imaging systems. A CT system is a X-ray device used to produce cross sectional images of organs. For instance, a CT system may be used to produce a series of cross sectional images of the human lung. Radiologists are able to examine these series of cross sectional images to diagnose pulmonary nodules.
Lung cancer is the leading cause of cancer mortality for American men and women. Currently the five-year survival rate for patients with lung cancer is less than 15%, whereas this rate for patients with localized and small cancer is improved at 48%. Accordingly, the detection of localized and small lung nodules is an important task for radiologists. Currently, however, only 15% of lung cancer patients are diagnosed at an early stage. For increasing the detection rate of early lung cancer, low-dose helical computed tomography (CT) has been employed in screening programs. Low-dose CT (LDCT) has been shown to be more sensitive than conventional chest radiographs in the detection of small lung nodules. It is therefore desirable for LDCTs to be used during initial examinations for the early detection of lung cancer in screening programs.
However, it is still difficult to detect very subtle nodules. Further, the interpretation of a large number of CT images is time-consuming for radiologists.
The above-mentioned deficiencies in the difficulty to detect very subtle nodules and the interpretation of a large number of CT images are mitigated by the embodiments of the present invention, which relate to a fully automated computerized scheme for the detection of subtle nodules by use of a novel subtraction CT technique. Embodiments of the present invention comprise creating a mask by linear interpolation of two warped CT section images. Other embodiments of the present invention comprise creating a mask image from a morphological filtered image, which is created from a plurality of CT section images.
In embodiments of the present invention, mask images are created for a particular CT section image. Mask images created such that subtraction of the mask image from the targeted CT section image reveals or highlights small lung nodules in the targeted CT section. In general, the mask image is created utilizing the targeted CT section image along with other CT section images generated from the same CT scan. Based on these other CT section images and the target section image, a mask image can be created that is very similar to the targeted CT section image, but without the presence of small lung nodules. Accordingly, when the mask image is subtracted from the targeted CT section image, the differences between the mask image and the targeted CT section image should reveal the small lung nodules. There are several embodiments of the present invention that accomplished the creation of a mask image.